A Novel image processing technique for weighted particle size distribution assessment.

The objective of the study was to create a reliable method that could be used to evaluate the particle size distribution of samples and pre-mixes in real-world situations, particularly those consisting of typical formulation blends. The goal was to use this method to assess the uniformity of the samples and ensure that they met the required quality standards. The researchers aimed to create a method that could be easily incorporated into the manufacturing process, providing a practical and efficient solution. This study demonstrates the use of ImageJ software to analyze the particle size distribution (PSD) of powders. The technique produces qualitative data from microscopy images and quantitative data from analysis of parameters including average diameter, D10, D50, D90, and standard deviation. The method was tested with various treatments, showing differentiating outcomes in all cases. The alternate technique provides a rapid and cost-effective method for PSD analysis, surpassing the limitations of sieve analysis. Extensive testing of the method, using a variety of sample types, including typical formulation blends, was performed. The results suggest that the method can effectively assess the morphology of changing materials during batch manufacturing and characterize uniformity in blends. The methodology has the capability to identify attributes related to PSD that are typically required to be monitored during manufacturing. The technique allows for accurate and reliable quantification of the attributes through image capture technology. The technique has future potential and has important implications for material science, powder rheology, pharmaceutical formulation development, and continual process monitoring.

[Figure: see text]A Novel Image Processing Technique for Weighted Particle Size Distribution Assessment.

A Quantitative Study of US FDA Inspection Data for Drug Manufacturing Sites.

The purpose of this study was to determine if any correlations could be identified from the form 483 observations issued by the US FDA during routine GMP inspections of drug substance and drug product manufacturing sites. The data analysed encompassed inspection observations from 2014 through 2018. The intent of the study is to provide unbiased insight to regulatory trends and to understand any underlying factors that may contribute to a sites non-compliance status. The data sets utilized in this study was derived from the US FDA public information database: Inspection Classification Database which was part of a 2009 open government initiative. The inspection observation data sets are ones that has been classified. The study results provide insights into domestic and foreign inspection trends. The assessment of 2014-2018 form 483 observations from FDA database revealed that the number of 483 forms issued has been on the increase however the number of observations has declined.

A Semiquantitative Risk Assessment Methodology Fit for Biopharmaceutical Life Cycle Stages.

This paper introduces an innovative risk assessment tool, a semiquantitative risk determination (SQRD) method designed to address risk on the operational and organizational level with a distinct patient safety perspective. Quality Risk Management (ICH Q9) is a systematic process for the assessment, control, communication, and review of risks to the quality of the drug (medicinal) product across the product life cycle. SQRD is a systematic data-driven risk assessment tool. It is of practical significance to have a risk assessment tool that directly links to patient safety attributes. The SQRD methodology has six distinctive steps that are customized to address patient impact and non-patient impact quality attributes. The target was to develop and utilize an advanced risk assessment tool that is reliable, robust, objective, and data-driven. SQRD can be applied to batch production, continuous process, or a hybrid of the two, and at any stage of the product life cycle such as early development, pilot formulation development, process validation, or commercial manufacturing. The output of SQRD can help in shaping and optimizing the product control strategy. The exercise enables systematic mitigation of the identified risks. The proposed SQRD tool systematically evaluates data and scientifically establishes reliable, robust, and efficient risk assessments.

A Science and Risk-Based Pragmatic Methodology for Blend and Content Uniformity Assessment.

This paper describes a pragmatic approach that can be applied in assessing powder blend and unit dosage uniformity of solid dose products at Process Design, Process Performance Qualification, and Continued/Ongoing Process Verification stages of the Process Validation lifecycle. The statistically based sampling, testing, and assessment plan was developed due to the withdrawal of the FDA draft guidance for industry "Powder Blends and Finished Dosage Units-Stratified In-Process Dosage Unit Sampling and Assessment." This paper compares the proposed Grouped Area Variance Estimate (GAVE) method with an alternate approach outlining the practicality and statistical rationalization using traditional sampling and analytical methods. The approach is designed to fit solid dose processes assuring high statistical confidence in both powder blend uniformity and dosage unit uniformity during all three stages of the lifecycle complying with ASTM standards as recommended by the US FDA.

Assessment Methodology for Process Validation Lifecycle Stage 3A.

The paper introduces evaluation methodologies and associated statistical approaches for process validation lifecycle Stage 3A. The assessment tools proposed can be applied to newly developed and launched small molecule as well as bio-pharma products, where substantial process and product knowledge has been gathered. The following elements may be included in Stage 3A: number of 3A batch determination; evaluation of critical material attributes, critical process parameters, critical quality attributes; in vivo in vitro correlation; estimation of inherent process variability (IPV) and PaCS index; process capability and quality dashboard (PCQd); and enhanced control strategy. US FDA guidance on Process Validation: General Principles and Practices, January 2011 encourages applying previous credible experience with suitably similar products and processes. A complete Stage 3A evaluation is a valuable resource for product development and future risk mitigation of similar products and processes. Elements of 3A assessment were developed to address industry and regulatory guidance requirements. The conclusions made provide sufficient information to make a scientific and risk-based decision on product robustness.

Stage 2 Process Performance Qualification (PPQ): a Scientific Approach to Determine the Number of PPQ Batches.

The approach documented in this article reviews data from earlier process validation lifecycle stages with a described statistical model to provide the "best estimate" on the number of process performance qualification (PPQ) batches that should generate sufficient information to make a scientific and risk-based decision on product robustness. This approach is based upon estimation of a statistical confidence from the current product knowledge (Stage 1), historical variability for similar products/processes (batch-to-batch), and label claim specifications such as strength. The analysis is to determine the confidence level with the measurements of the product quality attributes and to compare them with the specifications. The projected minimum number of PPQ batches required will vary depending on the product, process understanding, and attributes, which are critical input parameters for the current statistical model. This new approach considers the critical finished product CQAs (assay, dissolution, and content uniformity), primarily because assay/content uniformity and dissolution as well as strength are the components of the label claim. The key CQAs determine the number of PPQ batches. This approach will ensure that sufficient scientific data is generated to demonstrate process robustness as desired by the 2011 FDA guidance.

Acceptance Probability (P a) Analysis for Process Validation Lifecycle Stages.

This paper introduces an innovative statistical approach towards understanding how variation impacts the acceptance criteria of quality attributes. Because of more complex stage-wise acceptance criteria, traditional process capability measures are inadequate for general application in the pharmaceutical industry. The probability of acceptance concept provides a clear measure, derived from specific acceptance criteria for each quality attribute. In line with the 2011 FDA Guidance, this approach systematically evaluates data and scientifically establishes evidence that a process is capable of consistently delivering quality product. The probability of acceptance provides a direct and readily understandable indication of product risk. As with traditional capability indices, the acceptance probability approach assumes that underlying data distributions are normal. The computational solutions for dosage uniformity and dissolution acceptance criteria are readily applicable. For dosage uniformity, the expected AV range may be determined using the s lo and s hi values along with the worst case estimates of the mean. This approach permits a risk-based assessment of future batch performance of the critical quality attributes. The concept is also readily applicable to sterile/non sterile liquid dose products. Quality attributes such as deliverable volume and assay per spray have stage-wise acceptance that can be converted into an acceptance probability. Accepted statistical guidelines indicate processes with C pk > 1.33 as performing well within statistical control and those with C pk < 1.0 as "incapable" (1). A C pk > 1.33 is associated with a centered process that will statistically produce less than 63 defective units per million. This is equivalent to an acceptance probability of >99.99%.